• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• Journal of Gifted/Talented Education
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• v.11 no.3
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• pp.131-153
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• 2001

The importance of identifying gifted children during early childhood is becoming recognized. Nonetheless, most researchers preferred to study the primary and secondary levels where children are already and more clearly demonstrating what talents they have, and where more reliable predictions of gifted may be made. Comparatively lisle work has been done in this area. When we identify giftedness during early childhood, we have to consider the potential of the young children rather than on actual achievement. Giftedness during early childhood is still developing and less stable than that of older children and this prevents us from making firm and accurate predictions based on children's actual achievement. Dynamic assessment, based on Vygotsky's concept of the zone of proximal development(ZPD), suggests a new idea in the way the gifted young children are identified. In light of dynamic assessment, for identifying the potential giftedness of young children. we need to involve measuring both unassisted and assisted performance. Dynamic assessment usually consists of a test-intervene-retest format that focuses attention on the improvement in child performance when an adult provides mediated assistance on how to master the testing task. The advantages of the dynamic assessment are as follows: First, the dynamic assessment approach can provide a useful means for assessing young gifted child who have not demonstrated high ability on traditional identification method. Second, the dynamic assessment approach can assess the learning process of young children. Third, the dynamic assessment can lead an individualized education by the early identification of young gifted children. Fourth, the dynamic assessment can be a more accurate predictor of potential by linking diagnosis and instruction. Thus, it can make us provide an educational treatment effectively for young gifted children.

• Journal of Food Hygiene and Safety
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• v.31 no.1
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• pp.15-20
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• 2016

Malachite green was measured in 200 freshwater fish collected from local markets in Seoul using HPLC-DAD and LC-MS/MS. LC-MS/MS method was validated by linearity, accuracy, precision and limits of detection and quantification according to the CODEX's recommendation and HPLC-DAD method was applied according to the Food Code. Malachite green levels above the quantification limit of the LC-MS/MS were determined 18.5% (37) but just 1 fish was shown to contain malachite green by HPLC-DAD. Of 83 domestic fish, 21 fish were detected malachite green (25.3%). Of 117 fish from China, just 16 fish were detected malachite green (13.4%). In detection rate by species carp (35.0%), Crucian carp (30.4%), cat fish (28.0%), Korean bull head (23.8%), snake head (20.0%), eel (10.5%) and loach (7.8%) were in order. Especially, fish collected at summer were shown to contain malachite green frequently; the detection rate was 54.8%.

• Progress in Medical Physics
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• v.13 no.4
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• pp.209-217
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• 2002

There have been many studies on the application of the reciprocal advantages of multimodality image to define accurate target volume in the Process of radiation treatment planning. For the proper use of the multimodality images, the registration works between different modality images should be performed in advance. In this study, we selected chamfer matching method and mutual information method as most popular methods in recent image registration studies considering the registration accuracy and clinical practicality. And the two registration methods were analyzed to deduce the optimal registration method according to the characteristics of images. Lung phantom of which multimodality images could be acquired was fabricated and CT, MRI and SPECT images of the phantom were used in this study. We developed the registration program which can perform the two registration methods properly and analyzed the registration results which were produced by the developed program in many different images' conditions. Although the overall accuracy of the registration in both chamfer matching method and mutual information method was acceptable, the registration errors in SPECT images which had lower resolution and in degraded images of which data were removed in some part were increased when chamfer matching method was applied. Especially in the case of degraded reference image, chamfer matching methods produce relatively large errors compared with mutual information method. Mutual information method can be estimated as more robust registration method than chamfer matching method in this study because it did not need the prerequisite works, the extraction of accurate contour points, and it produced more accurate registration results consistently regardless of the images' characteristics. The analysis of the registration methods in this study can be expected to provide useful information to the utilization of multimodality images in delineating target volume for radiation treatment planning and in many other clinical applications.

• Journal of Chest Surgery
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• v.40 no.6 s.275
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• pp.414-419
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• 2007

Background: Replacing the ascending aorta is a standard surgical option for treating acute type A aortic dissection. But replacing the aortic arch has recently been reported as an acceptable procedure for this disease. We compared the effects of aortic arch replacement for treating acute type A aortic dissection with the effects of ascending aortic replacement. Material and Method: From 2002 to 2006, 25 patients undewent surgical treatment for acute type A aortic dissection, 12 patients undewent ascending aortic replacement and 13 patients underwent aortic arch replacement. Among the aortic arch group, an additional distal stent-graft was inserted during the operation in 5 patients. 19 patients (11 arch replaced patients and 8 ascending aortic replaced patients) were followed up at the out patient clinic for an average of $756{\pm}373$ days. All the patients undewent CT scanning and we analyzed their distal aortic segments. Result: 4 patients who underwent ascending aortic replacement died, so the overall mortality rate was 16%. Among the 11 long term followed-up arch replacement patients, 2 patients (18.1 %) developed distal aortic dilatation and one of them underwent thoracoabdominal aortic replacement later on. However, among the 8 the ascending aortic replaced patients, 5 patients (62.5%) developed distal aortic dilatation. Conclusion: Aortic arch replacement is one of the safe options for treating acute type A aortic dissection. Aortic arch replacement for treating acute type A aortic dissection could contribute to a reduced distal aortic dilatation rate and fewer secondary aortic procedures.

• Journal of The Korean Association For Science Education
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• v.41 no.6
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• pp.471-481
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• 2021

The importance of science education for cultivating the competencies required by an intelligent information society is gradually being strengthened. The government's roles and responsibilities for science education are stipulated by laws and policies in Korea. In order to systematically support science education, continuous monitoring of related policies is essential. This study aims to develop indicators that can be used to systematically and continuously monitor the national policies on science education in Korea. To achieve this goal, we first derive the framework for the indicators that has two dimensions (learner and science education context) and three categories (input, process, and outcome) from literature reviews. In order to derive the components and subcomponents of the indicators, the contents of science education-related indicators developed in Korea or abroad were reviewed. In order to verify the suitability and validity of the framework and components of the initial indicators, a two-round Delphi method was conducted with 25 expert participants with five different professions in science education. Finally, three components of the 'input' category (student characteristics, teacher characteristics, and educational infrastructure), three components of the 'process' category (science curriculum implementation, science educational contents and programs implementation, and teacher professional development program implementation), and five components of the 'outcome' category (science competency, participation and action, affective achievement, cognitive achievement, and satisfaction) were derived. An instrument to collect data from students, teachers, and institutions was developed based on the components and subcomponents, and content validity and internal consistency of the instrument were analyzed. Korea's Science Education Indicators developed in this study can comprehensively measure the current status of science education and is expected to contribute to a more efficient and effective science education policy planning and implementation.

• Korean Journal of Plant Resources
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• v.35 no.5
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• pp.594-606
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• 2022

This study was conducted to figure out the environment factors including vegetation structure and soil characteristics in natural habitats of Sageretia thea, and offers the basic information for habitats conservation and proliferation. The natural habitats of Sageretia thea were located at altitudes between 0~370 m with inclinations ranged as 3~35°. Through the vegetation research, the dominant species of tree layers were found to be divided into four communities. Cornus macrophylla (Com. I), Pinus thunbergii - Cinnamomum camphora (Com. II), Machilus thunbergii (Com. III), and Pinus thunbergii (Com. IV). The Species diversity (H') was 1.397~1.455, evenness (J') was 0.972~0.986, and dominance (D) was found to be 0.014~0.028. As a result of the physicochemical characteristics of soils, habitats soil mainly consisted of sandy soil and sandy loam soil. The average soil pH was 5.28~5.98, electronic conductivity was 0.22~63 ds/m, soil organic matter was 13.33~19.33 cmol⁺/kg, Exchange cations were appeared in the order of Ca²⁺, Mg²⁺, K⁺, and Na⁺. The Ordination result showed that Correlation coefficient between communities and environmental factors were significantly correlated with 4 main factors altitude, electronic conductivity, cation exchange capacity, exchangeable Na⁺. As expected, The result of this study will be helpful information on the preservation and mass production for use.

• (The) Research of the performance art and culture
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• no.22
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• pp.223-252
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• 2011

If we remember that modern world history has built systems of meaning through the concepts "difference," "different," and "other-ness" and has constructed new identity based on opposing hierarchy, music anthropology which tried to build "difference" between the west and the non-west was thoroughly west -centered, in the sense that it has perceived the heterogeneous symbolic systems among nations, as well as the barrier between the two cultures. On the other hand, world music, which has emerged as the most attractive field in culture industry and concert-art-market by crossing over global capitals, markets, and barriers, can be considered the most post-modernist and glocal. However, it is interesting to note that world music, which has been described as post-modern and glocal, has "difference" and "different" in its basis, just like the precepts for modern music anthropology (Meintjes 1990; Guilbault 1993; Taylor 1997; Frith 2000; Feld 1988). Furthermore, one can understand that the "different" and "difference," generally termed as being "non-western," are fundamentally based on ethnic or national imagination. In this sense it is interesting and important to examine such ethnic imagination in the "non-western ethnic musics" in music anthropology and in world music. Notwithstanding the attention paid and research made by music anthropologists, they have failed to elevate the "non-western ethnic musics" to become universally communicative, and these ethnic musics were reborn as "global" and "world music," through the process of "acculturation," "derivation," and "hybridization," with the west as major site for production and consumption. Meanwhile, the audience for world music, which did not exist before the birth of world music as a term, was now born as world music emerged. They are global populace who consume the musical "difference" and "imagined ethnicity," who through their consumption are constructing new social meanings including ethnicity, race, nation, and class identity. This study, by examining current discourse, performance, and process for the world music through media and field studies and scholarly debates, attempts to understand the production and consumption of "imagined ethnicity." This will also shed light on how "ethnicity" is created and consumed, and how this is involved in the process of world music.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.

• Journal of Food Hygiene and Safety
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• v.27 no.1
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• pp.42-49
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• 2012

This study was carried out to investigate the toxicity of food Red No.2 in the Sprague-Dawley (SD) female rat for 4 weeks. SD rats were orally administered for 28 days, with dosage of 500, 1,000, 2,000 mg/kg/day. Animals treated with food Red No.2 did not cause any death and show any clinical signs. They did not show any significant changes of body weight, feed uptake and water consumption. There were not significantly different from the control group in urinalysis, hematological, serum biochemical value and histopathological examination. In conclusion, 4 weeks of the repetitive oral medication of food Red No.2 has resulted no alteration of toxicity according to the test materials in the group of female rats with injection of 2,000 mg/kg. Therefore, food Red No.2 was not indicated to have any toxic effect in the SD rats, when it was orally administered below the dosage 2,000 mg/kg/day for 4 weeks.